I have had all sorts of fun of developing highly efficient and cost-effective systems and algorithms, including but not limited to: enabling and accelerating large-scale deep learning training on parallel/distributed/heterogeneous hardware, building ultra-fast inference engine, different types of model compression, large-scale data management. My research works have been published in major venues, including system and high-performance computing conferences (e.g., ASPLOS, NSDI, USENIX ATC, SC), and top-tier machine learning conferences (e.g., ICML, NeurIPS, ICLR, etc). Several of my work has been applied to Microsoft systems and products, such as Bing, Ads, Azure SQL, Windows, etc., leading to significant latency improvement and cost reduction.